Individuals' behavioral strategies are often well described by reaction norms, which are functions predicting repeatable patterns of personality, plasticity, and predictability across an environmental gradient. Reaction norms can be readily estimated using mixed-effects models and play a key role in current theories of adaptive individual variation. Unfortunately, however, it remains challenging to assess the effects of reaction norms on fitness-relevant outcomes, due to the high degree of uncertainty in random effect estimates of reaction norm parameters, also known as best linear unbiased predictors (BLUPs). Current approaches to this problem do not provide a generalized solution for modelling reaction norm effects with nonlinear structure, such as stabilizing, disruptive, balancing, and/or correlational selection, which are necessary for testing adaptive theory of individual variation. To address this issue, I present a novel solution for straightforward and unbiased estimation of linear and nonlinear reaction norm effects on fitness, applicable to both Gaussian and non-Gaussian measurements. This solution involves specifying BLUPs as random effects on behavior and fixed effects on fitness within a Bayesian multi-response model. By simultaneously accounting for uncertainty in reaction norm parameters and their causal effects on other measures, the risks accompanying classical approaches to BLUPs can be effectively avoided. I also introduce a new method for visualizing the consequences of multivariate selection on reaction norms. Simulations are then used to validate that the proposed models provide unbiased estimates across realistic parameter values, and an extensive coding tutorial is provided to aid researchers in applying this method to their own datasets in R.
Estimating Nonlinear Selection on Behavioral Reaction Norms
